LIVIA TRAN
E-mail: tran.livia@berkeley.edu
Cell: (402)-613-2736
Based in Oakland, California
Github: github.com/liviatran
LinkedIn: linkedin.com/in/liviatran
ABOUT ME
Hi there! I'm Livia.
Our modern world is booming with technology, and I'm hoping to contribute to its progression via analyzing big data through developing efficient software. I am seeking employment in either bioinformatics or data science.
A little about me:
I graduated from UC Berkeley with a Bachelor's in Molecular Biology in 2017. I'm currently a Biomedical Sciences Master's student at San Francisco State University, with an emphasis in data science.
I'm self-motivated and very passionate about learning -- I'm a big advocate of creating and furthering my own knowledge. I'm a quick study with a knack for problem solving, and I love sorting through big data by harnessing the power of computer science. My main languages are R, Python, and SQL, but I'm open and capable of learning new software. Although I'm fairly independent with projects and my work, I value communication and teamwork.
In my spare time, I like to read, program, and hang out with my dog, Thelma, and two cats, Albie and Cobalt. I'm also a Midwest transplant (I'm from Nebraska -- go Huskers!), avid podcast listener, and strong believer of putting cereal in before the milk.
WORK EXPERIENCE
BIOINFORMATICS INTERN - SOFTWARE DEVELOPMENT
CHILDREN'S HOSPITAL OAKLAND RESEARCH INSTITUTE
May 2018-present
Mentor: Dr. Steven J. Mack, Department of Genetics, Children’s Hospital of Oakland Research Institute
Dr. Mack’s lab harnesses the power of bioinformatic software tools to better understand the Human Leukocyte Antigen (HLA) loci, a set of highly polymorphic genes that are especially of interest because of their strong associations to disease and the immune system.
● Developed complex, precise code through R to research whether disease-associated motifs, specifically for Multiple Sclerosis, are found in non-core exon regions (i.e. exons that do not contribute to the Antigen Recognition Domain (ARD) for peptide binding and recognition. These non-core exon regions encode transmembrane and cytosolic structures).
● Formulated code to analyze big immunogenetic data via:
Parsing through Gene Feature Enumerations (GFEs) for HLA to isolate key disease-associated feature sequences
Searching for amino-acid motifs in the HLA transmembrane region that may impact tertiary protein structure
Inspecting nucleotide motifs in the 3’UTR of HLA for possible disease association
Note: All outputs from the code developed above were used to analyze actual case vs. control Multiple Sclerosis studies obtained from the UCSF Benioff Children’s Hospital in Oakland. After producing concise dataframes, further analysis to determine disease-association was carried out through the Bridging ImmunoGenomic Data Analysis Workflow Gaps (BIGDAWG) package in R. BIGDAWG performs statistical association tests for highly polymorphic data, such as HLA.
VETERINARY TECHNICIAN
June 2018 - present
Location: Good Samaritan Veterinary Hospital, San Leandro, CA
Exercised proper animal restraint for treatments
Drew blood for in-house and out-house blood panels
Sterilizing and cleaning surgical packs
Performed various professional services (anal gland expression, nail trims, injections, etc)
Animal radiography certified
UNDERGRADUATE RESEARCHER
COATES LAB
January 2016 - May 2017
Mentor: Dr. John Coates, Department of Plant and Microbial Biology, Energy Biosciences Institute
Through utilizing sulfide analogs, the Coates laboratory works on microbial communities to inhibit sulfidogenesis for bioremediation purposes.
Managed chemostat conditions for Desulfovibrio growth
Prepared various medias to test for optimal bacterial growth
Analyzed sulfate analogs, specifically perchlorate, through measuring IC50, or point at which
perchlorate inhibits 50% of microbial growth
Assembled layout for complex communities (i.e. which fermenters and aerobic/anaerobic
bacteria based on competition, as well as nutrient make up) simulating those found in ocean
waters to most accurately represent ocean life
Utilized restriction fragment length polymorphisms and genomic sequencing to observe
sulfate reducer responses to other microbes within assembled communities
Sequenced microbial genomes to detect SNP(s) and performed analyses on possible
energetic trade-offs
UNDERGRADUATE RESEARCHEREVOLAB
January 2015-May 2015
Mentor: Dr. Rosemary Gillespie, Department of Integrative Biology, Hilgard Hall at UC Berkeley
The Gillespie Lab studies sexual reproduction adaptation in Hawaiian Tetragnatha, a species of arachnid subject to geographical isolation in the Hawaiian Islands.
Designed various trial parameters to study ecological and molecular natures of adaptive radiation in Hawaiian Tetragnatha through usage of olfactometers
Recorded and analyzed subject movement towards control or molted female spiders, which provided insight into chemical cue recognition
Examined genetic composition of various Hawaiian Tetragnatha species based upon reproductive isolation Elucidated chemical pheromone structures via NMR
OTHER PROGRAMMING PROJECTS
Tracking Genomic Evolution in Helicobacter pylori
Nov 2017 – Dec 2018
BACKGROUND: Helicobacter pylori, a gastrointestinal disease causing bacteria,has colonized the gut of mankind since the first Out of Africa migration event, with different haplotypes forming as a result of human movement and divergence. My interest in studying the genomic evolution of H. pylori was to help us better understand the differences in their genomic sequences, how that relates to where the strain was found, and which nucleotide sites are seemingly targeted for mutation.
This project was programmed in R, with data manipulation and mining to analyze which nucleotide sites were targeted for mutation, what their statistical relevance was, and whether these strains were deleterious.
Determining Drastic Amino Acid Changes in Enterovirus
Oct 2017 – Nov 2017
BACKGROUND: Enterovirus is a positive sense, single-stranded RNA virus associated with diseases such as hand-foot-and-mouth disease, myocarditis, and aseptic meningitis. Viruses often undergo drastic amino acid changes, or a change from one amino acid group to another (EX: amino acid change from Alanine, a hydrophobic amino acid, to Lysine, a positively charged amino acid), to increase virulence or avoid host detection. I focused on detecting drastic amino acid changes in genomic sequences for VP1 and VP2, structural proteins located at the surface of the viral capsid, for Enterovirus A, B, and C.
This project utilized R to analyze large FASTA genomic datasets of genetic data for VP1 and VP2 for various countries, including China, Denmark, and Japan, to determine whether drastic amino acid changes occurred within and between populations, and how those changes affected virulence.
SKILLS
Computer Science Skills:
R
Python
SQL
Big Data analysis
Bioinformatics software development
Biology Skills:
Anaerobic microbial culturing
RNA extraction
Spectrophotometry
Gas chromatography
Thin layer chromatography
Western Blot
Serial dilution growth
PCR
RT-qPCR
Cell Sonification
Gel Electrophoresis
DNA extraction
Animal husbandry
Gram staining
Chemostat maintenance